US Patent Application 17727505. INTELLIGENT LOAD BALANCING IN A DISTRIBUTED SYSTEM simplified abstract
Contents
INTELLIGENT LOAD BALANCING IN A DISTRIBUTED SYSTEM
Organization Name
Inventor(s)
George Mathew of Belmont CA (US)
Mukesh K. Sharma of Wakad Pune (IN)
Murthy V. Mamidi of Queen Creek AZ (US)
INTELLIGENT LOAD BALANCING IN A DISTRIBUTED SYSTEM - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 17727505 Titled 'INTELLIGENT LOAD BALANCING IN A DISTRIBUTED SYSTEM'
Simplified Explanation
This abstract describes a method for load balancing in a distributed network filesystem. The network consists of multiple worker nodes that access shared resources controlled by distributed locks. To balance the workload, each worker node is assigned a range of directory entries. The worker nodes provide feedback to a load balancer, which includes information about the resources they have acquired and other parameters. Periodic updates are made to the directory ranges to ensure that complete subtrees of the directory tree fit within a single range assigned to a worker. When a client request is received to access a data asset, the load balancer selects the most suitable worker node based on their internal states and other parameters.
Original Abstract Submitted
Embodiments for performing load balancing in a filesystem of a distributed network comprising worker nodes accessing shared resources that are controlled by distributed locks to grant and prevent access from clients, by assigning a range of directory entries to each worker node, and providing feedback from each worker node to a load balancer to expose an internal state of acquired resources and other parameters of each worker node. In response to periodic updates of the directory ranges to be collocated, the system maintains directory ranges in each worker so that complete subtrees of the directory tree fit in a single range assigned to a worker. Upon receipt of a client request to access a data asset, the load balancer selects the most optimum worker to satisfy the request based on the worker internal states and other parameters.
